A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Journal: Journal of biomedical informatics
Published Date: Jun 20, 2025

Abstract

OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings.

Authors

  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Fang Li
    Department of General Surgery, Chongqing General Hospital, Chongqing, China.
  • Na Hong
    Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.
  • Manqi Li
    McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  • Kirk Roberts
    The University of Texas Health Science Center at Houston, USA.
  • Licong Cui
    The University of Texas Health Science Center at Houston, USA.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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